title: Advanced building energy management system demonstration for Department of Defense buildings.
abstract: This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success.
authors.affiliation: Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA. zoneill@eng.ua.edu
authors.forename: Zheng
authors.initials: Z
authors.lastname: O'Neill
authors.forename: Trevor
authors.initials: T
authors.lastname: Bailey
authors.forename: Bing
authors.initials: B
authors.lastname: Dong
authors.forename: Madhusudana
authors.initials: M
authors.lastname: Shashanka
authors.forename: Dong
authors.initials: D
authors.lastname: Luo
mesh_terms: D003246:Conservation of Energy Resources / Q000379:methods* / Q000639:trends; D005160:Facility Design and Construction / Q000379:methods* / Q000639:trends; D006801:Humans; D014481:United States; D059030:United States Department of Defense / Q000639:trends
keywords: building energy performance; energy diagnostics; energy performance visualization; reduced-order model